Incident Report for Anthropic
63 comments
·September 9, 2025metadat
dinfinity
Gemini 2.5 Pro is also pretty good, if you need a fallback.
behnamoh
> Unfortunate timing, as I am rooting for Anthropic as the underdog...
Give me a break... Anthropic has never been the underdog. Their CEO is one of the most hypocrite people in the field. In the name of "safety" and "ethics", they got away with not releasing even a single open-weight (or open-source) model, calling out OpenAI as the "bad guys", and constantly trying to sabotage pro-competition and pro-consumer AI laws in the US.
ahofmann
A company can be considered as the underdog and still be run by assholes?
cma
They were also the first to work with the NSA, years before their change to support military uses, according to Dean Ball, former Whitehouse AI Policy advisor, in an interview with Nathan Labenz.
nextworddev
Til
watwut
Well OpenAI and Sam Altman are "bad guys". At least that part is true. It is just that Anthropic is not better.
behnamoh
> Well OpenAI and Sam Altman are "bad guys".
Define "bad". Sama is a businessman and at least doesn't pretend to be a saint like Amodei does.
rfoo
You are absolutely right! But China bad Dario good Anthropic the only firm caring about AI safety /s
paulddraper
Rooting for the underdog is a moving target.
fxtentacle
My guess would be that they tried to save money with speculative decoding and they had too loose thresholds for the verification stage.
As someone who has implemented this myself, I know that it’s pretty easy to make innocent mistakes there. And the only visible result is a tiny distortion of the output distribution which only really becomes visible after analysing thousands of tokens. And I would assume that all providers are using speculative decoding by now because it’s the only way to have good inference speed at scale.
As a quick recap, you train a small model to quickly predict the easy tokens, like filler words, so that you can jump over them in the recurrent decoding loop. That way, a serial model can predict multiple tokens per invocation, thereby easily doubling throughput.
And the fact that they need lots of user tokens to verify that it works correctly would nicely explain why it took them a while to find and fix the issue.
metadat
Speculative Decoding, for the uninitiated (like me..): https://research.google/blog/looking-back-at-speculative-dec...
buildbot
Standard speculative decoding without relaxed acceptance has no accuracy impact as far as I understand things. If you always run the verification; you always have the true target model output.
fxtentacle
You need the relaxed acceptance to get those cost savings. Every time you determine your small model to be "good enough", it allows the large model to skip one iteration in its recursive decoding loop. You are correct in the sense that you can estimate the probability that the large model would have had for predicting the token(s) that your small model chose, but you don't know which tokens the large model might have predicted based on tokens that the small model did not predict. Unless, of course, your "small" model becomes as precise as the large model, at which point it's not small anymore.
In other words: The speculative decoding causes "holes" in your beam search data. You can fill them by sampling more, increasing hosting costs. Or you fill them with approximations, but that'll skew the results to be more "safe" => more generic, less reasoning.
irthomasthomas
"we often make changes intended to improve the efficiency and throughput of our models.." I thought Anthropic said they never mess with their models like this? Now they do it often?
jjani
They already have a track record of messing with internal system prompts (including those that affect the API) which obviously directly change outputs given the same prompts. So in effect, they've already been messing with the models for a long time. It's well known among founders who run services based on their products that this happened, everyone who does long output saw the same. It happened around November last year. If you had a set of evals running that expected an output of e.g. 6k tokens in length on 3.5 Sonnet, overnight it suddenly started cutting off at <2k, ending the message with something like "(Would you like me to continue?)". This is on raw API calls.
Never seen or heard of (from people running services at scale, not just rumours) this kind of API behaviour change for a the same model from OpenAI and Google. Gemini 2.5 Pro did materially change at time of prod release despite them claiming they had simply "promoted the final preview endpoint to GA", but in that case you can give them the benefit of it being technically a new endpoint. Still lying, but less severe.
simonw
Can you expand on "messing with internal system prompts" - this is the first I have heard of that.
jjani
We talked about this a few weeks ago so it can't be the first time you're hearing about this :) [1] You hadn't heard about it before because it really only affected API customers running services including calls that required output of 2.5k+ (rough estimate) tokens in a single message. Which is pretty much just the small subset of AI founders/developers that are in the long-form content space. And then out of those, the ones using Sonnet 3.5 at the time for these tasks, which is an even smaller number. Back then it wasn't as favoured yet as it is now, especially for content. It's also expensive for high-output tasks, so only relevant to high-margin services, mostly B2B. Most of us in that small group aren't posting online about it - we urgently work to work around it, as we did back then. Still, as I showed you, some others did post about it as well.
The only explanations were either internal system prompt changes, or updating the actual model. Since the only sharply different evals were those expecting 2.5k+ token outputs with all short ones remaining the same, and the consistency of the change was effectively 100%, it's unlikely to have been a stealth model update, though not impossible.
simonw
Anthropic have frequently claimed that they do not change the model weights without bumping the version number.
I think that is compatible with making "changes intended to improve the efficiency and throughput of our models" - i.e. optimizing their inference stack, but only if they do so in a way that doesn't affect model output quality.
Clearly they've not managed to do that recently, but they are at least treating these problems as bugs and rolling out fixes for them.
mccoyb
I read this as changes to quantization and batching techniques. The latter shouldn’t affect logits, the former definitely will …
naiv
I think this is directly related to https://x.com/sama/status/1965110064215458055
And I think it was 100% on purpose that they degraded the model performance as Claude Code got so popular and they either ran out of capacity or were losing money too fast.
But now that people are fleeing to Codex as it improved so much during the time, they had to act now.
rsanek
I wonder how long the myth of AI firms losing money on inference will persist. Feels like the majority of the evidence points to good margins on that side
disgruntledphd2
> I wonder how long the myth of AI firms losing money on inference will persist. Feels like the majority of the evidence points to good margins on that side
If they're not losing money on inference, then why do they need to keep raising absurd amounts of money? Like, if inference is profitable and they're still losing lots and lots of money, then training must be absurdly expensive, which means that basically they invest in quickly depreciating capital assets (the models) so not a good business.
I think Anthropic is an interesting case study here, as most of their volume is API and they don't have a very generous free tier (unlike OpenAI).
deepdarkforest
They will probably also release sonnet 4.2 or something soon to make people jump back again to try it and hopefully restick
null
andy_ppp
So they aren’t saying what the bug was that caused this issue? Would love a more detailed explanation, what could possibly cause the model degradation apart from potentially pointing the queries to the wrong model?
qsort
If I had to guess, something related to floating point operations. FP additions and multiplications are neither commutative nor associative.
visarga
This is why it is hard to take a subscription or dependency on them, if they degrade the services willy nilly. Bait and switch tactic.
In Cursor I am seeing varying degrees of delays after exhausting my points, for On-Demand Usage. Some days it works well, other days it just inserts a 30s wait on each message. What am I paying for? You never know when you buy.
behnamoh
You should never buy annual AI subs. This field moves so fast and companies often change their ToS. Poe.com did the same and I was out (one day they decided to change the quotas/month for the SOTA models and turned off the good old GPT-4 and replaced it with GPT-4-Turbo which was quantized and bad).
andrewinardeer
Could you not ask for, and be entitled to, a refund for your remaining time on an annual subscription if ToS change n months into it?
bakugo
Could you ask them? Sure, but good luck getting it. In theory, forcefully changing the terms of a service after payment without offering a refund should clearly not be allowed, but in practice, it very much is unless you're willing to waste disproportionate amounts of money taking them to court.
d4rkp4ttern
My biggest concern now is — if the issue they have is as vague as “reports of degraded quality”, how do they even approach fixing it? And what measurable criteria will they use, to declare that it is fixed? Would they take a vibes-check opinion poll?
Curious why they can’t run some benchmarks with the model (if they suspect the issue is with the model itself) or some agentic coding benchmarks on Claude-code (if the issue might be with the scaffolding, prompts etc).
naiv
The model providers should analyse the tone of the instructions.
Before I finally gave up on Claude Code, I noticed that I got more aggressive towards it, the more stupid it got as I could not believe how dumb it started to be.
And I am sure I was not the only one.
simianwords
There are loads of people who just used Claude and left unimpressed and moved on to something else. They would never know about this regression.
And this bad memory might stick for a while.
BhavdeepSethi
You're absolutely right! The degraded model quality finally pushed me to stop paying for the max plan. Still on the Pro for now.
mccoyb
Here’s a report: Claude Code (the software) is getting worse by the day.
Removing the shown token comsumption rates (which allowed understanding when tokens were actually being sent / received!) … sometimes hiding the compaction percentage … the incredible lag on ESC interruption on long running sessions, the now broken clearing of the context window content on TASK tool usage
Who the fuck is working on this software and do they actually use it themselves?
Maybe the quality of Claude Code on any given day is indicative of whether their models are degraded …
CuriouslyC
Claude Code is indeed legit bad. You'd never know that this was a billion dollar company by the mess of javascript they hacked together. You have to periodically close and re-open the client because otherwise it starts to lag the system from constantly scanning and saving a big JSON file, and they didn't think to shard their storage or use a database. I have 128GB of ram on my workstation and running 8 claude code instances at once sometimes causes heavy thrashing and desktop responsiveness issues... That's just insane.
Needless to say I built my own agent (just needs a good web UI, last step!). The only thing keeping me with anthropic right now is the economics of the plan, my inference bill would be a second mortgage without it.
pton_xd
Lately I've noticed even the Claude web interface for chat is laggy on my 16 core / 32 GB RAM laptop. How is that possible?! It's just text!
yurifury
Use /config and enable verbose output to see the token consumption/usage per message.
avishai2112
what kind of incident report is this ? “It’s a bug, we fixed it !” - Anthropic
Do they credit your account if you were impacted? Or it's just "sorry 'bout 'dat month of trash"?
Unfortunate timing, as I am rooting for Anthropic as the underdog, but feel compelled to use whatever works best. Since mid-August I've demoted Claude to only putting the fire on UIs and am getting amazing results with GPT-5 for everything else. Given the nonstop capacity warnings on codex cli, I might not be the only one.